MAR and UAR: Solutions for Depth Profile Reconstruction of MFL Inspection with High Degree of Freedoms

Magnetic Flux Leakage(MFL) inspection is one of the most efficient non-destructive testing method used to detect anormalies of ferromagnetic materials which is widely used in many industry scenarios. Sizing of the defect from inspection signal is the key problem which is also a classic inverse problem. Reconstruction of corrosion liked defect with high degree of freedom(DOF) is a challenging problem in this area. A multi-agent based reconstruction algorithm(MAR) is proposed in this paper. Instead of designing an algorithm to build the entire defect, the defect region is divided into several segments. Each segment is reconstructed by its corresponding agent. The dimension of reward function is reduced number-of-agent times from the DOF of defect which makes the critic network efficient. Base on MAR, an uni-agent base reconstruction algorithm(UAR) is also given as another solution which requires less computing resources. How to divide the defect and corresponding signal to achieve better performance is also studied. Algorithms proposed in this paper solves the problem of inverse problem of MFL inspection with high DOF. The effectiveness of the algorithms proposed in this paper are validated with simulated data and practical inspection data. The results show that the algorithms proposed in this paper has good reconstruction accuracy with robustness when dealing with defects with high DOF. Note to Practitioners-MFL inspection is the widely used pipeline in-line inspection method. Currently, defects detected are usually quantified as simple cube defects. Since most of the practical defects on industrial pipelines are caused by corrosion which have complex shape, how to quantify defects with more details is not only a research topic but also practical demand. In this paper, MAR and UAR are proposed to reconstruct the depth with 99 degree of freedoms. In addition, the algorithms proposed can be extended to reconstruct the depth with much more higher degree of freedoms by just adopting more agents. The impact of wings data on reconstruction results is also discussed in this paper. The algorithms proposed in this paper focus on 2-D dimension which considers only the length and depth reconstruction problem. In further research, we will try to give solutions to 3-D dimension problem which considers length, width and depth in the same time.